Host-specific HCV evolution and response to the combined interferon and ribavirin therapy

James Lara, John E. Tavis, Maureen J. Donlin, William M. Lee, He Jun Yuan, Brian L. Pearlman, Gilberto Vaughan, Joseph C. Forbi, Guo Liang Xia, Yury E. Khudyakov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Machine-learning methods in the form of Bayesian networks (BN), linear projection (LP) and self-organizing tree (SOT) models were used to explore association among polymorphic sites within the HVR1 and NS5a regions of the HCV genome, host demographic factors (ethnicity, gender and age) and response to the combined interferon (IFN) and ribavirin (RBV) therapy. The BN models predicted therapy outcomes, gender and ethnicity with accuracy of 90%, 90% and 88.9%, respectively. The LP and SOT models strongly confirmed associations of the HVR1 and NS5A structures with response to therapy and demographic host factors identified by BN. The data indicate host specificity of HCV evolution and suggest the application of these models to predict outcomes of IFN/RBV therapy.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Pages102-109
Number of pages8
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 - Atlanta, GA, United States
Duration: Nov 12 2011Nov 15 2011

Publication series

Name2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

Other

Other2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Country/TerritoryUnited States
CityAtlanta, GA
Period11/12/1111/15/11

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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